While a SOM-based classifier could be used in place of the neural or \(k\) NN classifiers proposed
originally [1]}, we propose a different approach that does not require
splitting the input data into training and testing sets and leverages the distribution of the
data on the output space instead. The intuition behind this is that if two samples are equally
distributed on the original space, they must be equally distributed on the output space.
